Summary
What you’ll impact
Our organization is hiring a Staff Machine Learning Engineer focused on retrieval systems to lead the Ads Retrieval ML team. The role involves defining technical vision, designing and deploying retrieval models, and collaborating across engineering, product, and advertising stakeholders while mentoring junior engineers.
Responsibilities
What you'll do
- Define the technical vision and multi-year roadmap for ads retrieval modeling in collaboration with cross-functional stakeholders.
- Design, develop, and deploy candidate-generation and retrieval models for Reddit’s advertising platforms.
- Apply modern retrieval techniques such as two-tower architectures, representation learning, embeddings, and deep learning methods to create meaningful product value.
- Enhance the retrieval stack by optimizing objectives, labels, sampling strategies, feature design, and candidate filtering processes.
- Work with approximate nearest-neighbor and vector search systems, balancing recall, relevance, latency, diversity, and cost considerations.
- Establish robust evaluation practices that connect retrieval metrics to downstream ad and user engagement outcomes.
- Lead offline analysis and online experiments, interpret ambiguous results, and iterate models based on insights.
- Collaborate with downstream ranking, ad platform, auction, measurement, and product teams to ensure effective integration of retrieval models.
- Document design decisions, review code and model updates, and uphold high standards for model quality, testing, and observability.
- Mentor and develop team members, fostering expertise in retrieval, recommendation, and representation learning.
Requirements
What you’ll bring
- 7+ years of industry experience with a strong background in applied machine learning.
- Extensive experience in information retrieval, candidate generation, recommender systems, or relevance modeling.
- Deep understanding of retrieval modeling concepts such as DNN, embeddings, two-tower models, and approximate nearest-neighbor search.
- Proficiency in training, evaluating, debugging, and deploying deep learning models using frameworks like TensorFlow or PyTorch.
- Proven track record of owning ML projects from problem framing to production deployment and iteration.
- Strong knowledge of experimental design, offline evaluation metrics, and their relationship to business and user outcomes.
- Experience working with large-scale behavioral or content datasets and complex feature pipelines.
- Excellent software engineering skills with the ability to produce reliable, maintainable production code.
- Technical leadership experience, including project management, influencing teams, and mentoring engineers.
- Exceptional communication skills to explain complex technical concepts to diverse audiences.